Genetic algorithm as a variable selection procedure for the simulation of 13C nuclear magnetic resonance spectra of flavonoid derivatives using multiple linear regression

In order to accurately simulate 13C NMR spectra of hydroxy, polyhydroxy and methoxy substituted flavonoid a quantitative structure–property relationship (QSPR) model, relating atom-based calculated descriptors to 13C NMR chemical shifts (ppm, TMS = 0), is developed. A dataset consisting of 50 flavonoid derivatives was employed for the present analysis. A set of 417 topological, geometrical, and electronic descriptors representing various structural characteristics was calculated and separate multilinear QSPR models were developed between each carbon atom of flavonoid and the calculated descriptors. Genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models. Analysis of the results revealed a correlation coefficient and root mean square error (RMSE) of 0.994 and 2.53 ppm, respectively, for the prediction set.